{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第七章 逻辑回归与采购决策"
   ]
  },
  {
   "attachments": {
    "e8382232-14c8-44a8-a9f4-120686e5cab2.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:e8382232-14c8-44a8-a9f4-120686e5cab2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step1，数据探索性分析\n",
    "违约率分析\n",
    "缺失值分析\n",
    "对于某个字段的统计分析（比如RevolvingUtilizationOfUnsecuredLines）\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.766127</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>0.802982</td>\n",
       "      <td>9120.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.957151</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0.121876</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.658180</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0.085113</td>\n",
       "      <td>3042.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.233810</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.036050</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.907239</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>0.024926</td>\n",
       "      <td>63588.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149995</th>\n",
       "      <td>0</td>\n",
       "      <td>0.040674</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0.225131</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149996</th>\n",
       "      <td>0</td>\n",
       "      <td>0.299745</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.716562</td>\n",
       "      <td>5584.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149997</th>\n",
       "      <td>0</td>\n",
       "      <td>0.246044</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>3870.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149998</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5716.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149999</th>\n",
       "      <td>0</td>\n",
       "      <td>0.850283</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.249908</td>\n",
       "      <td>8158.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150000 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  \\\n",
       "0                      1                              0.766127   45   \n",
       "1                      0                              0.957151   40   \n",
       "2                      0                              0.658180   38   \n",
       "3                      0                              0.233810   30   \n",
       "4                      0                              0.907239   49   \n",
       "...                  ...                                   ...  ...   \n",
       "149995                 0                              0.040674   74   \n",
       "149996                 0                              0.299745   44   \n",
       "149997                 0                              0.246044   58   \n",
       "149998                 0                              0.000000   30   \n",
       "149999                 0                              0.850283   64   \n",
       "\n",
       "        NumberOfTime30-59DaysPastDueNotWorse    DebtRatio  MonthlyIncome  \\\n",
       "0                                          2     0.802982         9120.0   \n",
       "1                                          0     0.121876         2600.0   \n",
       "2                                          1     0.085113         3042.0   \n",
       "3                                          0     0.036050         3300.0   \n",
       "4                                          1     0.024926        63588.0   \n",
       "...                                      ...          ...            ...   \n",
       "149995                                     0     0.225131         2100.0   \n",
       "149996                                     0     0.716562         5584.0   \n",
       "149997                                     0  3870.000000            NaN   \n",
       "149998                                     0     0.000000         5716.0   \n",
       "149999                                     0     0.249908         8158.0   \n",
       "\n",
       "        NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "0                                    13                        0   \n",
       "1                                     4                        0   \n",
       "2                                     2                        1   \n",
       "3                                     5                        0   \n",
       "4                                     7                        0   \n",
       "...                                 ...                      ...   \n",
       "149995                                4                        0   \n",
       "149996                                4                        0   \n",
       "149997                               18                        0   \n",
       "149998                                4                        0   \n",
       "149999                                8                        0   \n",
       "\n",
       "        NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "0                                  6                                     0   \n",
       "1                                  0                                     0   \n",
       "2                                  0                                     0   \n",
       "3                                  0                                     0   \n",
       "4                                  1                                     0   \n",
       "...                              ...                                   ...   \n",
       "149995                             1                                     0   \n",
       "149996                             1                                     0   \n",
       "149997                             1                                     0   \n",
       "149998                             0                                     0   \n",
       "149999                             2                                     0   \n",
       "\n",
       "        NumberOfDependents  \n",
       "0                      2.0  \n",
       "1                      1.0  \n",
       "2                      0.0  \n",
       "3                      0.0  \n",
       "4                      0.0  \n",
       "...                    ...  \n",
       "149995                 0.0  \n",
       "149996                 2.0  \n",
       "149997                 0.0  \n",
       "149998                 0.0  \n",
       "149999                 0.0  \n",
       "\n",
       "[150000 rows x 11 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.read_csv('./cs-training.csv')\n",
    "df_train = df_train.iloc[:,1:]\n",
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.885519</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0.177513</td>\n",
       "      <td>5700.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.463295</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0.527237</td>\n",
       "      <td>9141.0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.043275</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>0.687648</td>\n",
       "      <td>5083.0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.280308</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0.925961</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0.019917</td>\n",
       "      <td>3865.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101498</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.282653</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0.068522</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101499</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.922156</td>\n",
       "      <td>36</td>\n",
       "      <td>3</td>\n",
       "      <td>0.934217</td>\n",
       "      <td>7615.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101500</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.081596</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>836.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101501</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.335457</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>3568.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101502</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.441842</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0.198918</td>\n",
       "      <td>5916.0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101503 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  \\\n",
       "0                    NaN                              0.885519   43   \n",
       "1                    NaN                              0.463295   57   \n",
       "2                    NaN                              0.043275   59   \n",
       "3                    NaN                              0.280308   38   \n",
       "4                    NaN                              1.000000   27   \n",
       "...                  ...                                   ...  ...   \n",
       "101498               NaN                              0.282653   24   \n",
       "101499               NaN                              0.922156   36   \n",
       "101500               NaN                              0.081596   70   \n",
       "101501               NaN                              0.335457   56   \n",
       "101502               NaN                              0.441842   29   \n",
       "\n",
       "        NumberOfTime30-59DaysPastDueNotWorse    DebtRatio  MonthlyIncome  \\\n",
       "0                                          0     0.177513         5700.0   \n",
       "1                                          0     0.527237         9141.0   \n",
       "2                                          0     0.687648         5083.0   \n",
       "3                                          1     0.925961         3200.0   \n",
       "4                                          0     0.019917         3865.0   \n",
       "...                                      ...          ...            ...   \n",
       "101498                                     0     0.068522         1400.0   \n",
       "101499                                     3     0.934217         7615.0   \n",
       "101500                                     0   836.000000            NaN   \n",
       "101501                                     0  3568.000000            NaN   \n",
       "101502                                     0     0.198918         5916.0   \n",
       "\n",
       "        NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "0                                     4                        0   \n",
       "1                                    15                        0   \n",
       "2                                    12                        0   \n",
       "3                                     7                        0   \n",
       "4                                     4                        0   \n",
       "...                                 ...                      ...   \n",
       "101498                                5                        0   \n",
       "101499                                8                        0   \n",
       "101500                                3                        0   \n",
       "101501                                8                        0   \n",
       "101502                               12                        0   \n",
       "\n",
       "        NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "0                                  0                                     0   \n",
       "1                                  4                                     0   \n",
       "2                                  1                                     0   \n",
       "3                                  2                                     0   \n",
       "4                                  0                                     0   \n",
       "...                              ...                                   ...   \n",
       "101498                             0                                     0   \n",
       "101499                             2                                     0   \n",
       "101500                             0                                     0   \n",
       "101501                             2                                     1   \n",
       "101502                             0                                     0   \n",
       "\n",
       "        NumberOfDependents  \n",
       "0                      0.0  \n",
       "1                      2.0  \n",
       "2                      2.0  \n",
       "3                      0.0  \n",
       "4                      1.0  \n",
       "...                    ...  \n",
       "101498                 0.0  \n",
       "101499                 4.0  \n",
       "101500                 NaN  \n",
       "101501                 3.0  \n",
       "101502                 0.0  \n",
       "\n",
       "[101503 rows x 11 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv('./cs-test.csv')\n",
    "df_test = df_test.iloc[:,1:]\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 去除没用的Unnamed\n",
    "df_train.drop(columns=df_train.columns[0], inplace=True)\n",
    "df_test.drop(columns=df_test.columns[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    139974\n",
       "1     10026\n",
       "Name: SeriousDlqin2yrs, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.SeriousDlqin2yrs.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SeriousDlqin2yrs', ylabel='count'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 违约率SeriousDlqin2yrs进行可视化，90天以上逾期或更差\n",
    "import seaborn as sns\n",
    "sns.countplot(x='SeriousDlqin2yrs', data=df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06684"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 违约率\n",
    "df_train['SeriousDlqin2yrs'].sum()/len(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SeriousDlqin2yrs                            0\n",
       "RevolvingUtilizationOfUnsecuredLines        0\n",
       "age                                         0\n",
       "NumberOfTime30-59DaysPastDueNotWorse        0\n",
       "DebtRatio                                   0\n",
       "MonthlyIncome                           29731\n",
       "NumberOfOpenCreditLinesAndLoans             0\n",
       "NumberOfTimes90DaysLate                     0\n",
       "NumberRealEstateLoansOrLines                0\n",
       "NumberOfTime60-89DaysPastDueNotWorse        0\n",
       "NumberOfDependents                       3924\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 缺失值\n",
    "df_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    150000.000000\n",
       "mean          6.048438\n",
       "std         249.755371\n",
       "min           0.000000\n",
       "25%           0.029867\n",
       "50%           0.154181\n",
       "75%           0.559046\n",
       "max       50708.000000\n",
       "Name: RevolvingUtilizationOfUnsecuredLines, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看某一列的情况\n",
    "# 除房地产和汽车贷款等无分期付款债务外，信用卡和个人信用额度的总余额除以信贷限额\n",
    "df_train['RevolvingUtilizationOfUnsecuredLines'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SeriousDlqin2yrs                           0.066840\n",
       "RevolvingUtilizationOfUnsecuredLines       6.048438\n",
       "age                                       52.295207\n",
       "NumberOfTime30-59DaysPastDueNotWorse       0.421033\n",
       "DebtRatio                                353.005076\n",
       "MonthlyIncome                           6670.221237\n",
       "NumberOfOpenCreditLinesAndLoans            8.452760\n",
       "NumberOfTimes90DaysLate                    0.265973\n",
       "NumberRealEstateLoansOrLines               1.018240\n",
       "NumberOfTime60-89DaysPastDueNotWorse       0.240387\n",
       "NumberOfDependents                         0.757222\n",
       "dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='RevolvingUtilizationOfUnsecuredLines'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 直方图看分布\n",
    "sns.distplot(df_train['RevolvingUtilizationOfUnsecuredLines'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step2，数据缺失值填充，采用简单规则，如使用中位数进行填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SeriousDlqin2yrs                        0\n",
       "RevolvingUtilizationOfUnsecuredLines    0\n",
       "age                                     0\n",
       "NumberOfTime30-59DaysPastDueNotWorse    0\n",
       "DebtRatio                               0\n",
       "MonthlyIncome                           0\n",
       "NumberOfOpenCreditLinesAndLoans         0\n",
       "NumberOfTimes90DaysLate                 0\n",
       "NumberRealEstateLoansOrLines            0\n",
       "NumberOfTime60-89DaysPastDueNotWorse    0\n",
       "NumberOfDependents                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用中位数填充缺失值\n",
    "df_train = df_train.fillna(df_train.median())\n",
    "df_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step3，数据分箱\n",
    "1) age [-math.inf, 25, 40, 50, 60, 70, math.inf] age分成6断\n",
    "\n",
    "2) NumberOfDependents 家属人数 [-math.inf,2,4,6,8,10,math.inf] \n",
    "\n",
    "3) NumberOfTime30-59DaysPastDueNotWorse，NumberOfTime60-89DaysPastDueNotWorse，NumberOfTimes90DaysLate三种逾期 \n",
    " [-math.inf,1,2,3,4,5,6,7,8,9,math.inf]<br/>\n",
    "4) 其余字段 RevolvingUtilizationOfUnsecuredLines, DebtRatio, MonthlyIncome, NumberOfOpenCreditLinesAndLoans, NumberRealEstateLoansOrLines 分五段 qcut(data, q=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bin_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45</td>\n",
       "      <td>(40.0, 50.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40</td>\n",
       "      <td>(25.0, 40.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>(25.0, 40.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>(25.0, 40.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49</td>\n",
       "      <td>(40.0, 50.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149995</th>\n",
       "      <td>74</td>\n",
       "      <td>(70.0, inf]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149996</th>\n",
       "      <td>44</td>\n",
       "      <td>(40.0, 50.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149997</th>\n",
       "      <td>58</td>\n",
       "      <td>(50.0, 60.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149998</th>\n",
       "      <td>30</td>\n",
       "      <td>(25.0, 40.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149999</th>\n",
       "      <td>64</td>\n",
       "      <td>(60.0, 70.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        age       bin_age\n",
       "0        45  (40.0, 50.0]\n",
       "1        40  (25.0, 40.0]\n",
       "2        38  (25.0, 40.0]\n",
       "3        30  (25.0, 40.0]\n",
       "4        49  (40.0, 50.0]\n",
       "...     ...           ...\n",
       "149995   74   (70.0, inf]\n",
       "149996   44  (40.0, 50.0]\n",
       "149997   58  (50.0, 60.0]\n",
       "149998   30  (25.0, 40.0]\n",
       "149999   64  (60.0, 70.0]\n",
       "\n",
       "[150000 rows x 2 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将age进行分箱\n",
    "import math\n",
    "age_bins = [-math.inf, 25, 40, 50, 60, 70, math.inf]\n",
    "# 左开右闭\n",
    "df_train['bin_age'] = pd.cut(df_train['age'], bins=age_bins)\n",
    "df_train[['age','bin_age']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NumberOfDependents</th>\n",
       "      <th>NumberOfDependents_bins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>(-inf, 2.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>(-inf, 2.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>(-inf, 2.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>(-inf, 2.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>(-inf, 2.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149995</th>\n",
       "      <td>0.0</td>\n",
       "      <td>(-inf, 2.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149996</th>\n",
       "      <td>2.0</td>\n",
       "      <td>(-inf, 2.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149997</th>\n",
       "      <td>0.0</td>\n",
       "      <td>(-inf, 2.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149998</th>\n",
       "      <td>0.0</td>\n",
       "      <td>(-inf, 2.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149999</th>\n",
       "      <td>0.0</td>\n",
       "      <td>(-inf, 2.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        NumberOfDependents NumberOfDependents_bins\n",
       "0                      2.0             (-inf, 2.0]\n",
       "1                      1.0             (-inf, 2.0]\n",
       "2                      0.0             (-inf, 2.0]\n",
       "3                      0.0             (-inf, 2.0]\n",
       "4                      0.0             (-inf, 2.0]\n",
       "...                    ...                     ...\n",
       "149995                 0.0             (-inf, 2.0]\n",
       "149996                 2.0             (-inf, 2.0]\n",
       "149997                 0.0             (-inf, 2.0]\n",
       "149998                 0.0             (-inf, 2.0]\n",
       "149999                 0.0             (-inf, 2.0]\n",
       "\n",
       "[150000 rows x 2 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对NumberOfDependents 家属人数 进行分箱\n",
    "dependent_bins = [-math.inf,2,4,6,8,10,math.inf]\n",
    "# 左开右闭\n",
    "df_train['NumberOfDependents_bins'] = pd.cut(df_train['NumberOfDependents'], bins=dependent_bins)\n",
    "df_train[['NumberOfDependents','NumberOfDependents_bins']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bin_NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>bin_NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>bin_NumberOfTimes90DaysLate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(1.0, 2.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149995</th>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149996</th>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149997</th>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149998</th>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149999</th>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       bin_NumberOfTime30-59DaysPastDueNotWorse  \\\n",
       "0                                    (1.0, 2.0]   \n",
       "1                                   (-inf, 1.0]   \n",
       "2                                   (-inf, 1.0]   \n",
       "3                                   (-inf, 1.0]   \n",
       "4                                   (-inf, 1.0]   \n",
       "...                                         ...   \n",
       "149995                              (-inf, 1.0]   \n",
       "149996                              (-inf, 1.0]   \n",
       "149997                              (-inf, 1.0]   \n",
       "149998                              (-inf, 1.0]   \n",
       "149999                              (-inf, 1.0]   \n",
       "\n",
       "       bin_NumberOfTime60-89DaysPastDueNotWorse bin_NumberOfTimes90DaysLate  \n",
       "0                                   (-inf, 1.0]                 (-inf, 1.0]  \n",
       "1                                   (-inf, 1.0]                 (-inf, 1.0]  \n",
       "2                                   (-inf, 1.0]                 (-inf, 1.0]  \n",
       "3                                   (-inf, 1.0]                 (-inf, 1.0]  \n",
       "4                                   (-inf, 1.0]                 (-inf, 1.0]  \n",
       "...                                         ...                         ...  \n",
       "149995                              (-inf, 1.0]                 (-inf, 1.0]  \n",
       "149996                              (-inf, 1.0]                 (-inf, 1.0]  \n",
       "149997                              (-inf, 1.0]                 (-inf, 1.0]  \n",
       "149998                              (-inf, 1.0]                 (-inf, 1.0]  \n",
       "149999                              (-inf, 1.0]                 (-inf, 1.0]  \n",
       "\n",
       "[150000 rows x 3 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NumberOfTime30-59DaysPastDueNotWorse，NumberOfTime60-89DaysPastDueNotWorse，NumberOfTimes90DaysLate三种逾期\n",
    "dpd_bins =  [-math.inf,1,2,3,4,5,6,7,8,9,math.inf]\n",
    "df_train['bin_NumberOfTime30-59DaysPastDueNotWorse'] = pd.cut(df_train['NumberOfTime30-59DaysPastDueNotWorse'], bins=dpd_bins)\n",
    "df_train['bin_NumberOfTime60-89DaysPastDueNotWorse'] = pd.cut(df_train['NumberOfTime60-89DaysPastDueNotWorse'], bins=dpd_bins)\n",
    "df_train['bin_NumberOfTimes90DaysLate'] = pd.cut(df_train['NumberOfTimes90DaysLate'], bins=dpd_bins)\n",
    "# 查看分箱情况\n",
    "df_train[['bin_NumberOfTime30-59DaysPastDueNotWorse', 'bin_NumberOfTime60-89DaysPastDueNotWorse', 'bin_NumberOfTimes90DaysLate']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "      <th>bin_age</th>\n",
       "      <th>NumberOfDependents_bins</th>\n",
       "      <th>bin_NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>bin_NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>bin_NumberOfTimes90DaysLate</th>\n",
       "      <th>bin_RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>bin_DebtRatio</th>\n",
       "      <th>bin_MonthlyIncome</th>\n",
       "      <th>bin_NumberOfOpenCreditLinesAndLoans</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.766127</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>0.802982</td>\n",
       "      <td>9120.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>(40.0, 50.0]</td>\n",
       "      <td>(-inf, 2.0]</td>\n",
       "      <td>(1.0, 2.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(0.699, 50708.0]</td>\n",
       "      <td>(0.468, 4.0]</td>\n",
       "      <td>(8250.0, 3008750.0]</td>\n",
       "      <td>(12.0, 58.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.957151</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0.121876</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>(25.0, 40.0]</td>\n",
       "      <td>(-inf, 2.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(0.699, 50708.0]</td>\n",
       "      <td>(-0.001, 0.134]</td>\n",
       "      <td>(-0.001, 3400.0]</td>\n",
       "      <td>(-0.001, 4.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.658180</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0.085113</td>\n",
       "      <td>3042.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(25.0, 40.0]</td>\n",
       "      <td>(-inf, 2.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(0.271, 0.699]</td>\n",
       "      <td>(-0.001, 0.134]</td>\n",
       "      <td>(-0.001, 3400.0]</td>\n",
       "      <td>(-0.001, 4.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.233810</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.036050</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(25.0, 40.0]</td>\n",
       "      <td>(-inf, 2.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(0.0832, 0.271]</td>\n",
       "      <td>(-0.001, 0.134]</td>\n",
       "      <td>(-0.001, 3400.0]</td>\n",
       "      <td>(4.0, 6.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.907239</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>0.024926</td>\n",
       "      <td>63588.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(40.0, 50.0]</td>\n",
       "      <td>(-inf, 2.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(0.699, 50708.0]</td>\n",
       "      <td>(-0.001, 0.134]</td>\n",
       "      <td>(8250.0, 3008750.0]</td>\n",
       "      <td>(6.0, 9.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149995</th>\n",
       "      <td>0</td>\n",
       "      <td>0.040674</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0.225131</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(70.0, inf]</td>\n",
       "      <td>(-inf, 2.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(0.0192, 0.0832]</td>\n",
       "      <td>(0.134, 0.287]</td>\n",
       "      <td>(-0.001, 3400.0]</td>\n",
       "      <td>(-0.001, 4.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149996</th>\n",
       "      <td>0</td>\n",
       "      <td>0.299745</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.716562</td>\n",
       "      <td>5584.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>(40.0, 50.0]</td>\n",
       "      <td>(-inf, 2.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(0.271, 0.699]</td>\n",
       "      <td>(0.468, 4.0]</td>\n",
       "      <td>(5400.0, 8250.0]</td>\n",
       "      <td>(-0.001, 4.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149997</th>\n",
       "      <td>0</td>\n",
       "      <td>0.246044</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>3870.000000</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(50.0, 60.0]</td>\n",
       "      <td>(-inf, 2.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(0.0832, 0.271]</td>\n",
       "      <td>(4.0, 329664.0]</td>\n",
       "      <td>(3400.0, 5400.0]</td>\n",
       "      <td>(12.0, 58.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149998</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5716.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(25.0, 40.0]</td>\n",
       "      <td>(-inf, 2.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-0.001, 0.0192]</td>\n",
       "      <td>(-0.001, 0.134]</td>\n",
       "      <td>(5400.0, 8250.0]</td>\n",
       "      <td>(-0.001, 4.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149999</th>\n",
       "      <td>0</td>\n",
       "      <td>0.850283</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.249908</td>\n",
       "      <td>8158.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(60.0, 70.0]</td>\n",
       "      <td>(-inf, 2.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>(0.699, 50708.0]</td>\n",
       "      <td>(0.134, 0.287]</td>\n",
       "      <td>(5400.0, 8250.0]</td>\n",
       "      <td>(6.0, 9.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150000 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  \\\n",
       "0                      1                              0.766127   45   \n",
       "1                      0                              0.957151   40   \n",
       "2                      0                              0.658180   38   \n",
       "3                      0                              0.233810   30   \n",
       "4                      0                              0.907239   49   \n",
       "...                  ...                                   ...  ...   \n",
       "149995                 0                              0.040674   74   \n",
       "149996                 0                              0.299745   44   \n",
       "149997                 0                              0.246044   58   \n",
       "149998                 0                              0.000000   30   \n",
       "149999                 0                              0.850283   64   \n",
       "\n",
       "        NumberOfTime30-59DaysPastDueNotWorse    DebtRatio  MonthlyIncome  \\\n",
       "0                                          2     0.802982         9120.0   \n",
       "1                                          0     0.121876         2600.0   \n",
       "2                                          1     0.085113         3042.0   \n",
       "3                                          0     0.036050         3300.0   \n",
       "4                                          1     0.024926        63588.0   \n",
       "...                                      ...          ...            ...   \n",
       "149995                                     0     0.225131         2100.0   \n",
       "149996                                     0     0.716562         5584.0   \n",
       "149997                                     0  3870.000000         5400.0   \n",
       "149998                                     0     0.000000         5716.0   \n",
       "149999                                     0     0.249908         8158.0   \n",
       "\n",
       "        NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "0                                    13                        0   \n",
       "1                                     4                        0   \n",
       "2                                     2                        1   \n",
       "3                                     5                        0   \n",
       "4                                     7                        0   \n",
       "...                                 ...                      ...   \n",
       "149995                                4                        0   \n",
       "149996                                4                        0   \n",
       "149997                               18                        0   \n",
       "149998                                4                        0   \n",
       "149999                                8                        0   \n",
       "\n",
       "        NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "0                                  6                                     0   \n",
       "1                                  0                                     0   \n",
       "2                                  0                                     0   \n",
       "3                                  0                                     0   \n",
       "4                                  1                                     0   \n",
       "...                              ...                                   ...   \n",
       "149995                             1                                     0   \n",
       "149996                             1                                     0   \n",
       "149997                             1                                     0   \n",
       "149998                             0                                     0   \n",
       "149999                             2                                     0   \n",
       "\n",
       "        NumberOfDependents       bin_age NumberOfDependents_bins  \\\n",
       "0                      2.0  (40.0, 50.0]             (-inf, 2.0]   \n",
       "1                      1.0  (25.0, 40.0]             (-inf, 2.0]   \n",
       "2                      0.0  (25.0, 40.0]             (-inf, 2.0]   \n",
       "3                      0.0  (25.0, 40.0]             (-inf, 2.0]   \n",
       "4                      0.0  (40.0, 50.0]             (-inf, 2.0]   \n",
       "...                    ...           ...                     ...   \n",
       "149995                 0.0   (70.0, inf]             (-inf, 2.0]   \n",
       "149996                 2.0  (40.0, 50.0]             (-inf, 2.0]   \n",
       "149997                 0.0  (50.0, 60.0]             (-inf, 2.0]   \n",
       "149998                 0.0  (25.0, 40.0]             (-inf, 2.0]   \n",
       "149999                 0.0  (60.0, 70.0]             (-inf, 2.0]   \n",
       "\n",
       "       bin_NumberOfTime30-59DaysPastDueNotWorse  \\\n",
       "0                                    (1.0, 2.0]   \n",
       "1                                   (-inf, 1.0]   \n",
       "2                                   (-inf, 1.0]   \n",
       "3                                   (-inf, 1.0]   \n",
       "4                                   (-inf, 1.0]   \n",
       "...                                         ...   \n",
       "149995                              (-inf, 1.0]   \n",
       "149996                              (-inf, 1.0]   \n",
       "149997                              (-inf, 1.0]   \n",
       "149998                              (-inf, 1.0]   \n",
       "149999                              (-inf, 1.0]   \n",
       "\n",
       "       bin_NumberOfTime60-89DaysPastDueNotWorse bin_NumberOfTimes90DaysLate  \\\n",
       "0                                   (-inf, 1.0]                 (-inf, 1.0]   \n",
       "1                                   (-inf, 1.0]                 (-inf, 1.0]   \n",
       "2                                   (-inf, 1.0]                 (-inf, 1.0]   \n",
       "3                                   (-inf, 1.0]                 (-inf, 1.0]   \n",
       "4                                   (-inf, 1.0]                 (-inf, 1.0]   \n",
       "...                                         ...                         ...   \n",
       "149995                              (-inf, 1.0]                 (-inf, 1.0]   \n",
       "149996                              (-inf, 1.0]                 (-inf, 1.0]   \n",
       "149997                              (-inf, 1.0]                 (-inf, 1.0]   \n",
       "149998                              (-inf, 1.0]                 (-inf, 1.0]   \n",
       "149999                              (-inf, 1.0]                 (-inf, 1.0]   \n",
       "\n",
       "       bin_RevolvingUtilizationOfUnsecuredLines    bin_DebtRatio  \\\n",
       "0                              (0.699, 50708.0]     (0.468, 4.0]   \n",
       "1                              (0.699, 50708.0]  (-0.001, 0.134]   \n",
       "2                                (0.271, 0.699]  (-0.001, 0.134]   \n",
       "3                               (0.0832, 0.271]  (-0.001, 0.134]   \n",
       "4                              (0.699, 50708.0]  (-0.001, 0.134]   \n",
       "...                                         ...              ...   \n",
       "149995                         (0.0192, 0.0832]   (0.134, 0.287]   \n",
       "149996                           (0.271, 0.699]     (0.468, 4.0]   \n",
       "149997                          (0.0832, 0.271]  (4.0, 329664.0]   \n",
       "149998                         (-0.001, 0.0192]  (-0.001, 0.134]   \n",
       "149999                         (0.699, 50708.0]   (0.134, 0.287]   \n",
       "\n",
       "          bin_MonthlyIncome bin_NumberOfOpenCreditLinesAndLoans  \n",
       "0       (8250.0, 3008750.0]                        (12.0, 58.0]  \n",
       "1          (-0.001, 3400.0]                       (-0.001, 4.0]  \n",
       "2          (-0.001, 3400.0]                       (-0.001, 4.0]  \n",
       "3          (-0.001, 3400.0]                          (4.0, 6.0]  \n",
       "4       (8250.0, 3008750.0]                          (6.0, 9.0]  \n",
       "...                     ...                                 ...  \n",
       "149995     (-0.001, 3400.0]                       (-0.001, 4.0]  \n",
       "149996     (5400.0, 8250.0]                       (-0.001, 4.0]  \n",
       "149997     (3400.0, 5400.0]                        (12.0, 58.0]  \n",
       "149998     (5400.0, 8250.0]                       (-0.001, 4.0]  \n",
       "149999     (5400.0, 8250.0]                          (6.0, 9.0]  \n",
       "\n",
       "[150000 rows x 20 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RevolvingUtilizationOfUnsecuredLines, DebtRatio, MonthlyIncome, NumberOfOpenCreditLinesAndLoans, NumberRealEstateLoansOrLines 分五段\n",
    "df_train['bin_RevolvingUtilizationOfUnsecuredLines'] = pd.qcut(df_train.RevolvingUtilizationOfUnsecuredLines , q=5, duplicates='drop')\n",
    "df_train['bin_DebtRatio'] = pd.qcut(df_train.DebtRatio , q=5, duplicates='drop')\n",
    "df_train['bin_MonthlyIncome'] = pd.qcut(df_train.MonthlyIncome , q=5, duplicates='drop')\n",
    "df_train['bin_NumberOfOpenCreditLinesAndLoans'] = pd.qcut(df_train.NumberOfOpenCreditLinesAndLoans , q=5, duplicates='drop')\n",
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-inf, 0.0]    56188\n",
       "(0.0, 1.0]     52338\n",
       "(1.0, 2.0]     31522\n",
       "(2.0, 3.0]      6300\n",
       "(3.0, inf]      3652\n",
       "Name: bin_NumberRealEstateLoansOrLines, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 自定义规则分段\n",
    "loans_bins = [-math.inf, 0, 1, 2, 3, math.inf]\n",
    "df_train['bin_NumberRealEstateLoansOrLines'] = pd.cut(df_train.NumberRealEstateLoansOrLines , bins=loans_bins)\n",
    "df_train['bin_NumberRealEstateLoansOrLines'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bin_age',\n",
       " 'bin_NumberOfTime30-59DaysPastDueNotWorse',\n",
       " 'bin_NumberOfTime60-89DaysPastDueNotWorse',\n",
       " 'bin_NumberOfTimes90DaysLate',\n",
       " 'bin_RevolvingUtilizationOfUnsecuredLines',\n",
       " 'bin_DebtRatio',\n",
       " 'bin_MonthlyIncome',\n",
       " 'bin_NumberOfOpenCreditLinesAndLoans',\n",
       " 'bin_NumberRealEstateLoansOrLines']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计分箱字段\n",
    "bin_cols = [c for c in df_train.columns.values if c.startswith('bin_')]\n",
    "bin_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step4，特征筛选\n",
    "使用IV值衡量自变量的预测能力，筛选IV值>0.1的特征字段"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "# 计算IV，衡量变量的预测能力\n",
    "def cal_IV(df, feature, target):\n",
    "    lst = []\n",
    "    cols = ['Variable', 'Value', 'All', 'Bad']\n",
    "    # 对feature字段中的每个分箱的取值进行变量\n",
    "    for i in range(df[feature].nunique()): # unique代表不同的值，nunique = number of unique 不同值的个数\n",
    "        # feature字段的第i个分箱取值\n",
    "        val = list(df[feature].unique())[i]\n",
    "        # 统计feature， feature value， 这个value的个数，这个value导致target=1的个数\n",
    "#         print(feature)\n",
    "        lst.append([feature, val, len(df[df[feature]==val]), len(df[(df[feature]==val) & (df[target]==1)])])\n",
    "\n",
    "    data = pd.DataFrame(lst, columns=cols)\n",
    "    \n",
    "    # 筛选bad大于0的情况\n",
    "    data = data[data['Bad']>0]\n",
    "    data['Share'] = data['All'] / data['All'].sum() # 这个value所占比例\n",
    "    data['Bad Rate'] = data['Bad'] / data['All'] # 这个value导致bad情况，在该value个数的比例\n",
    "    data['Margin Bad'] = data['Bad'] / data['Bad'].sum() #  Margin Bad  bad占所有value的比例\n",
    "    data['Margin Good'] = (data['All'] - data['Bad']) / (data['All'] - data['Bad']).sum() \n",
    "    # 避免分子为0，导致-inf, log1p(x) = log(1+x), 这里NumberOfTime60-89DaysPastDueNotWorse第8分箱里bad为0\n",
    "    data['woe'] = np.log1p(data['Margin Bad'] / data['Margin Good'])\n",
    "    data['iv']  = ((data['Margin Bad'] - data['Margin Good']) * data['woe']).sum()\n",
    "    data.sort_values(by=['Variable', 'Value'], inplace=True)\n",
    "    \n",
    "    return (data['iv'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10630739209281982"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cal_IV(df_train, 'bin_age', 'SeriousDlqin2yrs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bin_age 0.10630739209281982\n",
      "bin_NumberOfTime30-59DaysPastDueNotWorse 0.3406105523141427\n",
      "bin_NumberOfTime60-89DaysPastDueNotWorse 0.2032930463579644\n",
      "bin_NumberOfTimes90DaysLate 0.3747036585092075\n",
      "bin_RevolvingUtilizationOfUnsecuredLines 0.5271795713507914\n",
      "bin_DebtRatio 0.031720271879942336\n",
      "bin_MonthlyIncome 0.028071864778230878\n",
      "bin_NumberOfOpenCreditLinesAndLoans 0.025007537468361242\n",
      "bin_NumberRealEstateLoansOrLines 0.03150600847435313\n"
     ]
    }
   ],
   "source": [
    "# 计算每个字段的IV值\n",
    "for f in bin_cols:\n",
    "    print(f, cal_IV(df_train, f, 'SeriousDlqin2yrs'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bin_age 0.24041120302785982\n",
      "bin_NumberOfDependents 0.01450836007644442\n",
      "bin_NumberOfTime30-59DaysPastDueNotWorse 0.492444774570198\n",
      "bin_NumberOfTime60-89DaysPastDueNotWorse 0.2665587583516951\n",
      "bin_NumberOfTimes90DaysLate 0.49160685733515563\n",
      "bin_RevolvingUtilizationOfUnsecuredLines 1.0596188771423887\n",
      "bin_DebtRatio 0.05948761145809681\n",
      "bin_MonthlyIncome 0.05623446147714756\n",
      "bin_NumberOfOpenCreditLinesAndLoans 0.04802315528985505\n",
      "bin_NumberRealEstateLoansOrLines 0.06167337290177645\n"
     ]
    }
   ],
   "source": [
    "# 计算每个字段的IV值\n",
    "for f in bin_cols:\n",
    "    print(f, cal_IV(df_train, f, 'SeriousDlqin2yrs'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "只选择iv>0.1的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 例如RevolvingUtilizationOfUnsecuredLines有很突出的数值，很容易导致违约，类似标签泄露"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    150000.000000\n",
       "mean          6.048438\n",
       "std         249.755371\n",
       "min           0.000000\n",
       "25%           0.029867\n",
       "50%           0.154181\n",
       "75%           0.559046\n",
       "max       50708.000000\n",
       "Name: RevolvingUtilizationOfUnsecuredLines, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['RevolvingUtilizationOfUnsecuredLines'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    139974\n",
       "1     10026\n",
       "Name: SeriousDlqin2yrs, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 总的违约10026\n",
    "df_train.SeriousDlqin2yrs.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 只选择IV>0.1的字段：\n",
    "* bin_age\n",
    "* bin_NumberOfTime30-59DaysPastDueNotWorse\n",
    "* bin_NumberOfTime60-89DaysPastDueNotWorse\n",
    "* bin_NumberOfTimes90DaysLate\n",
    "* bin_RevolvingUtilizationOfUnsecuredLines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step5，对于筛选出来的特征，计算每个bin的WOE值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保留的字段\n",
    "feature_cols = ['bin_NumberOfTime30-59DaysPastDueNotWorse','bin_NumberOfTime60-89DaysPastDueNotWorse','bin_NumberOfTimes90DaysLate','bin_RevolvingUtilizationOfUnsecuredLines','bin_age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算这些特征的woe\n",
    "def cal_WOE(df, features, target):\n",
    "    df_new = df.copy()\n",
    "    for f in features:\n",
    "        df_woe = df_new.groupby(f).agg({target:['sum', 'count']})\n",
    "        df_woe.columns = list(map(''.join, df_woe.columns.values))\n",
    "        df_woe = df_woe.reset_index()\n",
    "        df_woe = df_woe.rename(columns={target+'sum':'bad', target+'count':'all'})\n",
    "        # print(df_woe)\n",
    "        df_woe['good'] = df_woe['all'] - df_woe['bad']\n",
    "        df_woe['Margin Bad'] = df_woe['bad'] / df_woe['bad'].sum() \n",
    "        df_woe['Margin Good'] = df_woe['good'] / df_woe['good'].sum()\n",
    "        # 1p避免分母为0\n",
    "        df_woe['woe'] = np.log1p(df_woe['Margin Bad'] / df_woe['Margin Good'])\n",
    "        # 避免重名\n",
    "        df_woe.columns = [c if c==f else c+'_'+f for c in list(df_woe.columns.values)]\n",
    "        df_new = df_new.merge(df_woe, on=f, how='left')\n",
    "#         print(df_woe)\n",
    "    return df_new\n",
    "# 计算这些特征的WOE\n",
    "df_woe = cal_WOE(df_train, bin_cols, 'SeriousDlqin2yrs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>...</th>\n",
       "      <th>good_bin_NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>Margin Bad_bin_NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>Margin Good_bin_NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>woe_bin_NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>bad_bin_NumberRealEstateLoansOrLines</th>\n",
       "      <th>all_bin_NumberRealEstateLoansOrLines</th>\n",
       "      <th>good_bin_NumberRealEstateLoansOrLines</th>\n",
       "      <th>Margin Bad_bin_NumberRealEstateLoansOrLines</th>\n",
       "      <th>Margin Good_bin_NumberRealEstateLoansOrLines</th>\n",
       "      <th>woe_bin_NumberRealEstateLoansOrLines</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.766127</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>0.802982</td>\n",
       "      <td>9120.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>25838</td>\n",
       "      <td>0.184121</td>\n",
       "      <td>0.184591</td>\n",
       "      <td>0.691873</td>\n",
       "      <td>419</td>\n",
       "      <td>3652</td>\n",
       "      <td>3233</td>\n",
       "      <td>0.041791</td>\n",
       "      <td>0.023097</td>\n",
       "      <td>1.032961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.957151</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0.121876</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>30556</td>\n",
       "      <td>0.309495</td>\n",
       "      <td>0.218298</td>\n",
       "      <td>0.882845</td>\n",
       "      <td>4672</td>\n",
       "      <td>56188</td>\n",
       "      <td>51516</td>\n",
       "      <td>0.465988</td>\n",
       "      <td>0.368040</td>\n",
       "      <td>0.818076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.658180</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0.085113</td>\n",
       "      <td>3042.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>30556</td>\n",
       "      <td>0.309495</td>\n",
       "      <td>0.218298</td>\n",
       "      <td>0.882845</td>\n",
       "      <td>4672</td>\n",
       "      <td>56188</td>\n",
       "      <td>51516</td>\n",
       "      <td>0.465988</td>\n",
       "      <td>0.368040</td>\n",
       "      <td>0.818076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.233810</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.036050</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>24972</td>\n",
       "      <td>0.156892</td>\n",
       "      <td>0.178405</td>\n",
       "      <td>0.630962</td>\n",
       "      <td>4672</td>\n",
       "      <td>56188</td>\n",
       "      <td>51516</td>\n",
       "      <td>0.465988</td>\n",
       "      <td>0.368040</td>\n",
       "      <td>0.818076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.907239</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>0.024926</td>\n",
       "      <td>63588.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>35145</td>\n",
       "      <td>0.201177</td>\n",
       "      <td>0.251082</td>\n",
       "      <td>0.588475</td>\n",
       "      <td>2748</td>\n",
       "      <td>52338</td>\n",
       "      <td>49590</td>\n",
       "      <td>0.274087</td>\n",
       "      <td>0.354280</td>\n",
       "      <td>0.573037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149995</th>\n",
       "      <td>0</td>\n",
       "      <td>0.040674</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0.225131</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>30556</td>\n",
       "      <td>0.309495</td>\n",
       "      <td>0.218298</td>\n",
       "      <td>0.882845</td>\n",
       "      <td>2748</td>\n",
       "      <td>52338</td>\n",
       "      <td>49590</td>\n",
       "      <td>0.274087</td>\n",
       "      <td>0.354280</td>\n",
       "      <td>0.573037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149996</th>\n",
       "      <td>0</td>\n",
       "      <td>0.299745</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.716562</td>\n",
       "      <td>5584.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>30556</td>\n",
       "      <td>0.309495</td>\n",
       "      <td>0.218298</td>\n",
       "      <td>0.882845</td>\n",
       "      <td>2748</td>\n",
       "      <td>52338</td>\n",
       "      <td>49590</td>\n",
       "      <td>0.274087</td>\n",
       "      <td>0.354280</td>\n",
       "      <td>0.573037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149997</th>\n",
       "      <td>0</td>\n",
       "      <td>0.246044</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>3870.000000</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>25838</td>\n",
       "      <td>0.184121</td>\n",
       "      <td>0.184591</td>\n",
       "      <td>0.691873</td>\n",
       "      <td>2748</td>\n",
       "      <td>52338</td>\n",
       "      <td>49590</td>\n",
       "      <td>0.274087</td>\n",
       "      <td>0.354280</td>\n",
       "      <td>0.573037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149998</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5716.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>30556</td>\n",
       "      <td>0.309495</td>\n",
       "      <td>0.218298</td>\n",
       "      <td>0.882845</td>\n",
       "      <td>4672</td>\n",
       "      <td>56188</td>\n",
       "      <td>51516</td>\n",
       "      <td>0.465988</td>\n",
       "      <td>0.368040</td>\n",
       "      <td>0.818076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149999</th>\n",
       "      <td>0</td>\n",
       "      <td>0.850283</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.249908</td>\n",
       "      <td>8158.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>35145</td>\n",
       "      <td>0.201177</td>\n",
       "      <td>0.251082</td>\n",
       "      <td>0.588475</td>\n",
       "      <td>1765</td>\n",
       "      <td>31522</td>\n",
       "      <td>29757</td>\n",
       "      <td>0.176042</td>\n",
       "      <td>0.212589</td>\n",
       "      <td>0.603269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150000 rows × 75 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  \\\n",
       "0                      1                              0.766127   45   \n",
       "1                      0                              0.957151   40   \n",
       "2                      0                              0.658180   38   \n",
       "3                      0                              0.233810   30   \n",
       "4                      0                              0.907239   49   \n",
       "...                  ...                                   ...  ...   \n",
       "149995                 0                              0.040674   74   \n",
       "149996                 0                              0.299745   44   \n",
       "149997                 0                              0.246044   58   \n",
       "149998                 0                              0.000000   30   \n",
       "149999                 0                              0.850283   64   \n",
       "\n",
       "        NumberOfTime30-59DaysPastDueNotWorse    DebtRatio  MonthlyIncome  \\\n",
       "0                                          2     0.802982         9120.0   \n",
       "1                                          0     0.121876         2600.0   \n",
       "2                                          1     0.085113         3042.0   \n",
       "3                                          0     0.036050         3300.0   \n",
       "4                                          1     0.024926        63588.0   \n",
       "...                                      ...          ...            ...   \n",
       "149995                                     0     0.225131         2100.0   \n",
       "149996                                     0     0.716562         5584.0   \n",
       "149997                                     0  3870.000000         5400.0   \n",
       "149998                                     0     0.000000         5716.0   \n",
       "149999                                     0     0.249908         8158.0   \n",
       "\n",
       "        NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "0                                    13                        0   \n",
       "1                                     4                        0   \n",
       "2                                     2                        1   \n",
       "3                                     5                        0   \n",
       "4                                     7                        0   \n",
       "...                                 ...                      ...   \n",
       "149995                                4                        0   \n",
       "149996                                4                        0   \n",
       "149997                               18                        0   \n",
       "149998                                4                        0   \n",
       "149999                                8                        0   \n",
       "\n",
       "        NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "0                                  6                                     0   \n",
       "1                                  0                                     0   \n",
       "2                                  0                                     0   \n",
       "3                                  0                                     0   \n",
       "4                                  1                                     0   \n",
       "...                              ...                                   ...   \n",
       "149995                             1                                     0   \n",
       "149996                             1                                     0   \n",
       "149997                             1                                     0   \n",
       "149998                             0                                     0   \n",
       "149999                             2                                     0   \n",
       "\n",
       "        ...  good_bin_NumberOfOpenCreditLinesAndLoans  \\\n",
       "0       ...                                     25838   \n",
       "1       ...                                     30556   \n",
       "2       ...                                     30556   \n",
       "3       ...                                     24972   \n",
       "4       ...                                     35145   \n",
       "...     ...                                       ...   \n",
       "149995  ...                                     30556   \n",
       "149996  ...                                     30556   \n",
       "149997  ...                                     25838   \n",
       "149998  ...                                     30556   \n",
       "149999  ...                                     35145   \n",
       "\n",
       "       Margin Bad_bin_NumberOfOpenCreditLinesAndLoans  \\\n",
       "0                                            0.184121   \n",
       "1                                            0.309495   \n",
       "2                                            0.309495   \n",
       "3                                            0.156892   \n",
       "4                                            0.201177   \n",
       "...                                               ...   \n",
       "149995                                       0.309495   \n",
       "149996                                       0.309495   \n",
       "149997                                       0.184121   \n",
       "149998                                       0.309495   \n",
       "149999                                       0.201177   \n",
       "\n",
       "       Margin Good_bin_NumberOfOpenCreditLinesAndLoans  \\\n",
       "0                                             0.184591   \n",
       "1                                             0.218298   \n",
       "2                                             0.218298   \n",
       "3                                             0.178405   \n",
       "4                                             0.251082   \n",
       "...                                                ...   \n",
       "149995                                        0.218298   \n",
       "149996                                        0.218298   \n",
       "149997                                        0.184591   \n",
       "149998                                        0.218298   \n",
       "149999                                        0.251082   \n",
       "\n",
       "       woe_bin_NumberOfOpenCreditLinesAndLoans  \\\n",
       "0                                     0.691873   \n",
       "1                                     0.882845   \n",
       "2                                     0.882845   \n",
       "3                                     0.630962   \n",
       "4                                     0.588475   \n",
       "...                                        ...   \n",
       "149995                                0.882845   \n",
       "149996                                0.882845   \n",
       "149997                                0.691873   \n",
       "149998                                0.882845   \n",
       "149999                                0.588475   \n",
       "\n",
       "       bad_bin_NumberRealEstateLoansOrLines  \\\n",
       "0                                       419   \n",
       "1                                      4672   \n",
       "2                                      4672   \n",
       "3                                      4672   \n",
       "4                                      2748   \n",
       "...                                     ...   \n",
       "149995                                 2748   \n",
       "149996                                 2748   \n",
       "149997                                 2748   \n",
       "149998                                 4672   \n",
       "149999                                 1765   \n",
       "\n",
       "       all_bin_NumberRealEstateLoansOrLines  \\\n",
       "0                                      3652   \n",
       "1                                     56188   \n",
       "2                                     56188   \n",
       "3                                     56188   \n",
       "4                                     52338   \n",
       "...                                     ...   \n",
       "149995                                52338   \n",
       "149996                                52338   \n",
       "149997                                52338   \n",
       "149998                                56188   \n",
       "149999                                31522   \n",
       "\n",
       "       good_bin_NumberRealEstateLoansOrLines  \\\n",
       "0                                       3233   \n",
       "1                                      51516   \n",
       "2                                      51516   \n",
       "3                                      51516   \n",
       "4                                      49590   \n",
       "...                                      ...   \n",
       "149995                                 49590   \n",
       "149996                                 49590   \n",
       "149997                                 49590   \n",
       "149998                                 51516   \n",
       "149999                                 29757   \n",
       "\n",
       "       Margin Bad_bin_NumberRealEstateLoansOrLines  \\\n",
       "0                                         0.041791   \n",
       "1                                         0.465988   \n",
       "2                                         0.465988   \n",
       "3                                         0.465988   \n",
       "4                                         0.274087   \n",
       "...                                            ...   \n",
       "149995                                    0.274087   \n",
       "149996                                    0.274087   \n",
       "149997                                    0.274087   \n",
       "149998                                    0.465988   \n",
       "149999                                    0.176042   \n",
       "\n",
       "       Margin Good_bin_NumberRealEstateLoansOrLines  \\\n",
       "0                                          0.023097   \n",
       "1                                          0.368040   \n",
       "2                                          0.368040   \n",
       "3                                          0.368040   \n",
       "4                                          0.354280   \n",
       "...                                             ...   \n",
       "149995                                     0.354280   \n",
       "149996                                     0.354280   \n",
       "149997                                     0.354280   \n",
       "149998                                     0.368040   \n",
       "149999                                     0.212589   \n",
       "\n",
       "       woe_bin_NumberRealEstateLoansOrLines  \n",
       "0                                  1.032961  \n",
       "1                                  0.818076  \n",
       "2                                  0.818076  \n",
       "3                                  0.818076  \n",
       "4                                  0.573037  \n",
       "...                                     ...  \n",
       "149995                             0.573037  \n",
       "149996                             0.573037  \n",
       "149997                             0.573037  \n",
       "149998                             0.818076  \n",
       "149999                             0.603269  \n",
       "\n",
       "[150000 rows x 75 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_woe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_cols = ['NumberOfTime30-59DaysPastDueNotWorse','NumberOfTime60-89DaysPastDueNotWorse','NumberOfTimes90DaysLate','RevolvingUtilizationOfUnsecuredLines','age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features</th>\n",
       "      <th>bin</th>\n",
       "      <th>woe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NumberOfTime30-59DaysPastDueNotWorse</td>\n",
       "      <td>(1.0, 2.0]</td>\n",
       "      <td>1.797837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NumberOfTime30-59DaysPastDueNotWorse</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>0.572521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NumberOfTime30-59DaysPastDueNotWorse</td>\n",
       "      <td>(2.0, 3.0]</td>\n",
       "      <td>2.151185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>NumberOfTime30-59DaysPastDueNotWorse</td>\n",
       "      <td>(3.0, 4.0]</td>\n",
       "      <td>2.429111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>NumberOfTime30-59DaysPastDueNotWorse</td>\n",
       "      <td>(4.0, 5.0]</td>\n",
       "      <td>2.520613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>NumberOfTime30-59DaysPastDueNotWorse</td>\n",
       "      <td>(6.0, 7.0]</td>\n",
       "      <td>2.774776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>NumberOfTime30-59DaysPastDueNotWorse</td>\n",
       "      <td>(9.0, inf]</td>\n",
       "      <td>2.902860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1052</th>\n",
       "      <td>NumberOfTime30-59DaysPastDueNotWorse</td>\n",
       "      <td>(5.0, 6.0]</td>\n",
       "      <td>2.812612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6909</th>\n",
       "      <td>NumberOfTime30-59DaysPastDueNotWorse</td>\n",
       "      <td>(7.0, 8.0]</td>\n",
       "      <td>2.024184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10822</th>\n",
       "      <td>NumberOfTime30-59DaysPastDueNotWorse</td>\n",
       "      <td>(8.0, 9.0]</td>\n",
       "      <td>2.077007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NumberOfTime60-89DaysPastDueNotWorse</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>0.645352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>NumberOfTime60-89DaysPastDueNotWorse</td>\n",
       "      <td>(1.0, 2.0]</td>\n",
       "      <td>2.712133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>NumberOfTime60-89DaysPastDueNotWorse</td>\n",
       "      <td>(4.0, 5.0]</td>\n",
       "      <td>3.159234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1146</th>\n",
       "      <td>NumberOfTime60-89DaysPastDueNotWorse</td>\n",
       "      <td>(2.0, 3.0]</td>\n",
       "      <td>2.955438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1733</th>\n",
       "      <td>NumberOfTime60-89DaysPastDueNotWorse</td>\n",
       "      <td>(9.0, inf]</td>\n",
       "      <td>2.886833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2406</th>\n",
       "      <td>NumberOfTime60-89DaysPastDueNotWorse</td>\n",
       "      <td>(3.0, 4.0]</td>\n",
       "      <td>3.164917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6664</th>\n",
       "      <td>NumberOfTime60-89DaysPastDueNotWorse</td>\n",
       "      <td>(5.0, 6.0]</td>\n",
       "      <td>3.758483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16642</th>\n",
       "      <td>NumberOfTime60-89DaysPastDueNotWorse</td>\n",
       "      <td>(6.0, 7.0]</td>\n",
       "      <td>2.915139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23964</th>\n",
       "      <td>NumberOfTime60-89DaysPastDueNotWorse</td>\n",
       "      <td>(7.0, 8.0]</td>\n",
       "      <td>2.705454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68976</th>\n",
       "      <td>NumberOfTime60-89DaysPastDueNotWorse</td>\n",
       "      <td>(8.0, 9.0]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NumberOfTimes90DaysLate</td>\n",
       "      <td>(-inf, 1.0]</td>\n",
       "      <td>0.608707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NumberOfTimes90DaysLate</td>\n",
       "      <td>(2.0, 3.0]</td>\n",
       "      <td>2.998746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>NumberOfTimes90DaysLate</td>\n",
       "      <td>(1.0, 2.0]</td>\n",
       "      <td>2.701853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>NumberOfTimes90DaysLate</td>\n",
       "      <td>(4.0, 5.0]</td>\n",
       "      <td>3.224503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1713</th>\n",
       "      <td>NumberOfTimes90DaysLate</td>\n",
       "      <td>(3.0, 4.0]</td>\n",
       "      <td>3.379582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1733</th>\n",
       "      <td>NumberOfTimes90DaysLate</td>\n",
       "      <td>(9.0, inf]</td>\n",
       "      <td>2.878935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2910</th>\n",
       "      <td>NumberOfTimes90DaysLate</td>\n",
       "      <td>(8.0, 9.0]</td>\n",
       "      <td>3.691154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3400</th>\n",
       "      <td>NumberOfTimes90DaysLate</td>\n",
       "      <td>(5.0, 6.0]</td>\n",
       "      <td>3.088387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3929</th>\n",
       "      <td>NumberOfTimes90DaysLate</td>\n",
       "      <td>(6.0, 7.0]</td>\n",
       "      <td>4.140397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5684</th>\n",
       "      <td>NumberOfTimes90DaysLate</td>\n",
       "      <td>(7.0, 8.0]</td>\n",
       "      <td>3.580814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RevolvingUtilizationOfUnsecuredLines</td>\n",
       "      <td>(0.699, 50708.0]</td>\n",
       "      <td>1.495914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RevolvingUtilizationOfUnsecuredLines</td>\n",
       "      <td>(0.271, 0.699]</td>\n",
       "      <td>0.720083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RevolvingUtilizationOfUnsecuredLines</td>\n",
       "      <td>(0.0832, 0.271]</td>\n",
       "      <td>0.350952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>RevolvingUtilizationOfUnsecuredLines</td>\n",
       "      <td>(-0.001, 0.0192]</td>\n",
       "      <td>0.243890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>RevolvingUtilizationOfUnsecuredLines</td>\n",
       "      <td>(0.0192, 0.0832]</td>\n",
       "      <td>0.211221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>(40.0, 50.0]</td>\n",
       "      <td>0.813822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>age</td>\n",
       "      <td>(25.0, 40.0]</td>\n",
       "      <td>0.955231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>age</td>\n",
       "      <td>(70.0, inf]</td>\n",
       "      <td>0.279404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>age</td>\n",
       "      <td>(50.0, 60.0]</td>\n",
       "      <td>0.651655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>age</td>\n",
       "      <td>(60.0, 70.0]</td>\n",
       "      <td>0.406848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>age</td>\n",
       "      <td>(-inf, 25.0]</td>\n",
       "      <td>1.013134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   features               bin       woe\n",
       "0      NumberOfTime30-59DaysPastDueNotWorse        (1.0, 2.0]  1.797837\n",
       "1      NumberOfTime30-59DaysPastDueNotWorse       (-inf, 1.0]  0.572521\n",
       "13     NumberOfTime30-59DaysPastDueNotWorse        (2.0, 3.0]  2.151185\n",
       "183    NumberOfTime30-59DaysPastDueNotWorse        (3.0, 4.0]  2.429111\n",
       "191    NumberOfTime30-59DaysPastDueNotWorse        (4.0, 5.0]  2.520613\n",
       "251    NumberOfTime30-59DaysPastDueNotWorse        (6.0, 7.0]  2.774776\n",
       "423    NumberOfTime30-59DaysPastDueNotWorse        (9.0, inf]  2.902860\n",
       "1052   NumberOfTime30-59DaysPastDueNotWorse        (5.0, 6.0]  2.812612\n",
       "6909   NumberOfTime30-59DaysPastDueNotWorse        (7.0, 8.0]  2.024184\n",
       "10822  NumberOfTime30-59DaysPastDueNotWorse        (8.0, 9.0]  2.077007\n",
       "0      NumberOfTime60-89DaysPastDueNotWorse       (-inf, 1.0]  0.645352\n",
       "186    NumberOfTime60-89DaysPastDueNotWorse        (1.0, 2.0]  2.712133\n",
       "423    NumberOfTime60-89DaysPastDueNotWorse        (4.0, 5.0]  3.159234\n",
       "1146   NumberOfTime60-89DaysPastDueNotWorse        (2.0, 3.0]  2.955438\n",
       "1733   NumberOfTime60-89DaysPastDueNotWorse        (9.0, inf]  2.886833\n",
       "2406   NumberOfTime60-89DaysPastDueNotWorse        (3.0, 4.0]  3.164917\n",
       "6664   NumberOfTime60-89DaysPastDueNotWorse        (5.0, 6.0]  3.758483\n",
       "16642  NumberOfTime60-89DaysPastDueNotWorse        (6.0, 7.0]  2.915139\n",
       "23964  NumberOfTime60-89DaysPastDueNotWorse        (7.0, 8.0]  2.705454\n",
       "68976  NumberOfTime60-89DaysPastDueNotWorse        (8.0, 9.0]  0.000000\n",
       "0                   NumberOfTimes90DaysLate       (-inf, 1.0]  0.608707\n",
       "13                  NumberOfTimes90DaysLate        (2.0, 3.0]  2.998746\n",
       "186                 NumberOfTimes90DaysLate        (1.0, 2.0]  2.701853\n",
       "1298                NumberOfTimes90DaysLate        (4.0, 5.0]  3.224503\n",
       "1713                NumberOfTimes90DaysLate        (3.0, 4.0]  3.379582\n",
       "1733                NumberOfTimes90DaysLate        (9.0, inf]  2.878935\n",
       "2910                NumberOfTimes90DaysLate        (8.0, 9.0]  3.691154\n",
       "3400                NumberOfTimes90DaysLate        (5.0, 6.0]  3.088387\n",
       "3929                NumberOfTimes90DaysLate        (6.0, 7.0]  4.140397\n",
       "5684                NumberOfTimes90DaysLate        (7.0, 8.0]  3.580814\n",
       "0      RevolvingUtilizationOfUnsecuredLines  (0.699, 50708.0]  1.495914\n",
       "2      RevolvingUtilizationOfUnsecuredLines    (0.271, 0.699]  0.720083\n",
       "3      RevolvingUtilizationOfUnsecuredLines   (0.0832, 0.271]  0.350952\n",
       "11     RevolvingUtilizationOfUnsecuredLines  (-0.001, 0.0192]  0.243890\n",
       "14     RevolvingUtilizationOfUnsecuredLines  (0.0192, 0.0832]  0.211221\n",
       "0                                       age      (40.0, 50.0]  0.813822\n",
       "1                                       age      (25.0, 40.0]  0.955231\n",
       "5                                       age       (70.0, inf]  0.279404\n",
       "6                                       age      (50.0, 60.0]  0.651655\n",
       "15                                      age      (60.0, 70.0]  0.406848\n",
       "19                                      age      (-inf, 25.0]  1.013134"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 得到WOE规则 feature, bin, woe\n",
    "df_bin_to_woe = pd.DataFrame(columns=['features', 'bin', 'woe'])\n",
    "for f in feature_cols:\n",
    "    b = 'bin_' + f\n",
    "    w = 'woe_bin_' + f\n",
    "    df = df_woe[[w, b]].drop_duplicates()\n",
    "    df.columns = ['woe', 'bin']\n",
    "    df['features'] = f\n",
    "    df_bin_to_woe = pd.concat([df_bin_to_woe, df])\n",
    "\n",
    "df_bin_to_woe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step6，使用逻辑回归进行建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['woe_bin_age',\n",
       " 'woe_bin_NumberOfTime30-59DaysPastDueNotWorse',\n",
       " 'woe_bin_NumberOfTime60-89DaysPastDueNotWorse',\n",
       " 'woe_bin_NumberOfTimes90DaysLate',\n",
       " 'woe_bin_RevolvingUtilizationOfUnsecuredLines',\n",
       " 'woe_bin_DebtRatio',\n",
       " 'woe_bin_MonthlyIncome',\n",
       " 'woe_bin_NumberOfOpenCreditLinesAndLoans',\n",
       " 'woe_bin_NumberRealEstateLoansOrLines']"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "woe_cols = [c for c in df_woe.columns if 'woe' in c]\n",
    "woe_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据切分\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(df_woe[woe_cols], df_woe['SeriousDlqin2yrs'], test_size=0.2, random_state=33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>woe_bin_age</th>\n",
       "      <th>woe_bin_NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>woe_bin_NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>woe_bin_NumberOfTimes90DaysLate</th>\n",
       "      <th>woe_bin_RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>woe_bin_DebtRatio</th>\n",
       "      <th>woe_bin_MonthlyIncome</th>\n",
       "      <th>woe_bin_NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>woe_bin_NumberRealEstateLoansOrLines</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>106833</th>\n",
       "      <td>0.813822</td>\n",
       "      <td>0.572521</td>\n",
       "      <td>0.645352</td>\n",
       "      <td>0.608707</td>\n",
       "      <td>0.243890</td>\n",
       "      <td>0.651857</td>\n",
       "      <td>0.882076</td>\n",
       "      <td>0.882845</td>\n",
       "      <td>0.573037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126865</th>\n",
       "      <td>0.955231</td>\n",
       "      <td>0.572521</td>\n",
       "      <td>0.645352</td>\n",
       "      <td>0.608707</td>\n",
       "      <td>0.243890</td>\n",
       "      <td>0.597328</td>\n",
       "      <td>0.698081</td>\n",
       "      <td>0.882845</td>\n",
       "      <td>0.818076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21011</th>\n",
       "      <td>0.406848</td>\n",
       "      <td>0.572521</td>\n",
       "      <td>0.645352</td>\n",
       "      <td>0.608707</td>\n",
       "      <td>0.243890</td>\n",
       "      <td>0.613576</td>\n",
       "      <td>0.643114</td>\n",
       "      <td>0.630962</td>\n",
       "      <td>0.573037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31844</th>\n",
       "      <td>0.406848</td>\n",
       "      <td>0.572521</td>\n",
       "      <td>0.645352</td>\n",
       "      <td>0.608707</td>\n",
       "      <td>0.211221</td>\n",
       "      <td>0.928274</td>\n",
       "      <td>0.882076</td>\n",
       "      <td>0.633824</td>\n",
       "      <td>0.603269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133133</th>\n",
       "      <td>0.955231</td>\n",
       "      <td>0.572521</td>\n",
       "      <td>0.645352</td>\n",
       "      <td>0.608707</td>\n",
       "      <td>1.495914</td>\n",
       "      <td>0.597328</td>\n",
       "      <td>0.698081</td>\n",
       "      <td>0.882845</td>\n",
       "      <td>0.818076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34877</th>\n",
       "      <td>0.955231</td>\n",
       "      <td>0.572521</td>\n",
       "      <td>0.645352</td>\n",
       "      <td>0.608707</td>\n",
       "      <td>1.495914</td>\n",
       "      <td>0.645506</td>\n",
       "      <td>0.882076</td>\n",
       "      <td>0.882845</td>\n",
       "      <td>0.818076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147558</th>\n",
       "      <td>0.813822</td>\n",
       "      <td>1.797837</td>\n",
       "      <td>0.645352</td>\n",
       "      <td>0.608707</td>\n",
       "      <td>0.720083</td>\n",
       "      <td>0.928274</td>\n",
       "      <td>0.882076</td>\n",
       "      <td>0.633824</td>\n",
       "      <td>0.603269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75971</th>\n",
       "      <td>0.279404</td>\n",
       "      <td>0.572521</td>\n",
       "      <td>0.645352</td>\n",
       "      <td>0.608707</td>\n",
       "      <td>0.211221</td>\n",
       "      <td>0.597328</td>\n",
       "      <td>0.698081</td>\n",
       "      <td>0.630962</td>\n",
       "      <td>0.818076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131650</th>\n",
       "      <td>0.813822</td>\n",
       "      <td>0.572521</td>\n",
       "      <td>0.645352</td>\n",
       "      <td>0.608707</td>\n",
       "      <td>0.350952</td>\n",
       "      <td>0.645506</td>\n",
       "      <td>0.698081</td>\n",
       "      <td>0.630962</td>\n",
       "      <td>0.818076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104152</th>\n",
       "      <td>0.813822</td>\n",
       "      <td>0.572521</td>\n",
       "      <td>0.645352</td>\n",
       "      <td>0.608707</td>\n",
       "      <td>1.495914</td>\n",
       "      <td>0.651857</td>\n",
       "      <td>0.882076</td>\n",
       "      <td>0.882845</td>\n",
       "      <td>0.818076</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        woe_bin_age  woe_bin_NumberOfTime30-59DaysPastDueNotWorse  \\\n",
       "106833     0.813822                                      0.572521   \n",
       "126865     0.955231                                      0.572521   \n",
       "21011      0.406848                                      0.572521   \n",
       "31844      0.406848                                      0.572521   \n",
       "133133     0.955231                                      0.572521   \n",
       "...             ...                                           ...   \n",
       "34877      0.955231                                      0.572521   \n",
       "147558     0.813822                                      1.797837   \n",
       "75971      0.279404                                      0.572521   \n",
       "131650     0.813822                                      0.572521   \n",
       "104152     0.813822                                      0.572521   \n",
       "\n",
       "        woe_bin_NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "106833                                      0.645352   \n",
       "126865                                      0.645352   \n",
       "21011                                       0.645352   \n",
       "31844                                       0.645352   \n",
       "133133                                      0.645352   \n",
       "...                                              ...   \n",
       "34877                                       0.645352   \n",
       "147558                                      0.645352   \n",
       "75971                                       0.645352   \n",
       "131650                                      0.645352   \n",
       "104152                                      0.645352   \n",
       "\n",
       "        woe_bin_NumberOfTimes90DaysLate  \\\n",
       "106833                         0.608707   \n",
       "126865                         0.608707   \n",
       "21011                          0.608707   \n",
       "31844                          0.608707   \n",
       "133133                         0.608707   \n",
       "...                                 ...   \n",
       "34877                          0.608707   \n",
       "147558                         0.608707   \n",
       "75971                          0.608707   \n",
       "131650                         0.608707   \n",
       "104152                         0.608707   \n",
       "\n",
       "        woe_bin_RevolvingUtilizationOfUnsecuredLines  woe_bin_DebtRatio  \\\n",
       "106833                                      0.243890           0.651857   \n",
       "126865                                      0.243890           0.597328   \n",
       "21011                                       0.243890           0.613576   \n",
       "31844                                       0.211221           0.928274   \n",
       "133133                                      1.495914           0.597328   \n",
       "...                                              ...                ...   \n",
       "34877                                       1.495914           0.645506   \n",
       "147558                                      0.720083           0.928274   \n",
       "75971                                       0.211221           0.597328   \n",
       "131650                                      0.350952           0.645506   \n",
       "104152                                      1.495914           0.651857   \n",
       "\n",
       "        woe_bin_MonthlyIncome  woe_bin_NumberOfOpenCreditLinesAndLoans  \\\n",
       "106833               0.882076                                 0.882845   \n",
       "126865               0.698081                                 0.882845   \n",
       "21011                0.643114                                 0.630962   \n",
       "31844                0.882076                                 0.633824   \n",
       "133133               0.698081                                 0.882845   \n",
       "...                       ...                                      ...   \n",
       "34877                0.882076                                 0.882845   \n",
       "147558               0.882076                                 0.633824   \n",
       "75971                0.698081                                 0.630962   \n",
       "131650               0.698081                                 0.630962   \n",
       "104152               0.882076                                 0.882845   \n",
       "\n",
       "        woe_bin_NumberRealEstateLoansOrLines  \n",
       "106833                              0.573037  \n",
       "126865                              0.818076  \n",
       "21011                               0.573037  \n",
       "31844                               0.603269  \n",
       "133133                              0.818076  \n",
       "...                                      ...  \n",
       "34877                               0.818076  \n",
       "147558                              0.603269  \n",
       "75971                               0.818076  \n",
       "131650                              0.818076  \n",
       "104152                              0.818076  \n",
       "\n",
       "[120000 rows x 9 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9360333333333334\n",
      "0.7706007341913056\n"
     ]
    }
   ],
   "source": [
    "# 利用LR模型预测 \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, accuracy_score, roc_auc_score\n",
    "lr = LogisticRegression(random_state=33)\n",
    "model = lr.fit(x_train, y_train)\n",
    "y_pred = model.predict(x_test)\n",
    "print(accuracy_score(y_pred, y_test))\n",
    "print(roc_auc_score(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.0 64-bit ('Bi_env': venv)",
   "language": "python",
   "name": "python38064bitbienvvenvba07af95a1bb4b078aa8134bba84dff2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  },
  "toc-autonumbering": false
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
